2022-05-10

Data set overview

Title: “Peripheral Blood Mitochondrial DNA Copy Number Is Associated with Prostate Cancer Risk and Tumor Burden”
Authors: Zhou W. et. al. (2014)
Purpose: Predict cancer from biomarkers, mainly mtDNA

Loading

  • Dimensions: 392, 13

  • Control and cancer case groups are proportional

Cleaning

  • Check for duplicates

  • Filter for PCR success (pcr_success)

  • New dimensions: 387, 13

Augmenting

  • BMI- and DFI-classifier

  • New columns based on TNM-notation

  • Add ‘group’ as strings

  • New dimensions: 387, 18

Flowchart for project flow

Boxplot with continuous variables, any outliers?

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Boxplot with discrete variables, any outliers?

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Boxplot with discrete variables, any outliers?

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Boxplot with discrete variables, any outliers?

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Boxplot with discrete variables, any outliers?

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Re-creating plot from the article

A better biomarker for prostate cancer?

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>>>>>>> c82739fe0dbeabe4c0688fb8aac163cc643b0ee1 >>>>>>> 88874d76f4015e195047cc58f5c287b04ecb03ac >>>>>>> f9bed0730c00860f2fa64521553188e771a0c578

Logistic regression, excl. PSA

Significant p-values:
Maybe the distribution of DFI-classes are skewed?

Logistic regression, incl. PSA

Significant p-values:

Principal component analysis (PCA)

PCAPCAPCA

PCA

Some more data exploration

Interesting finding during exploratory data analysis

Conclusion

  • We can support the conclusion of the article, mtDNA is a biomarker for prostate cancer (e.g, it is reproducible)
  • PSA levels seem to be an even better biomarker
  • Both of the above could be supported by logistic regression
  • Conclusion for PCA?
  • Did we find anything interesting for EDA?